Two-dimensional profiling of carriers in a buried heterostructure multi-quantum-well laser: Calibrated scanning spreading resistance microscopy and scanning capacitance microscopy
Bibliographic record
Abstract
We report results of a scanning spreading resistance microscopy (SSRM) and scanning capacitance microscopy (SCM) study of the distribution of charge carriers inside multi-quantum-well (MQW) buried heterostructure (BH) lasers. We demonstrate that individual quantum-well–barrier layers can be resolved using high-resolution SSRM. Calibrated SSRM and SCM measurements were performed on the MQW BH laser structure, by utilizing known InP dopant staircase samples to calibrate the instrumentation. Doping concentrations derived from SSRM and SCM measurements were compared with the nominal values of both p- and n-doped regions in the MQW BH lasers. For n-type materials, the accuracy was bias dependent with SSRM, while for SCM, excellent quantitative agreement between measured and nominal dopant values was obtained. The SSRM was able to measure the dopant concentration in the p-type materials with ∼30% accuracy, but quantitative measurements could not be obtained with the SCM. Our results demonstrate the utility of combining calibrated SSRM and SCM to delineate quantitatively the transverse cross-sectional structure of complex two-dimensional devices such as MQW BH lasers, in which traditional one-dimensional probing using secondary ion mass spectroscopy provides only a partial picture of internal device structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".